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Record W4394144459 · doi:10.6084/m9.figshare.23261047

Flourishing chancelloriids from the Cambrian Kaili Biota of South China

2023· dataset· en· W4394144459 on OpenAlexaff
Tingzu Peng, Yuning Yang, Hao Yun, Xinglian Yang, Qianqian Zhang, Min He, Xiangri Chi, Jing Liu, Xi Liu

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFlourishingBiotaChinaPaleontologyGeologyGeographyEcologyArchaeologyBiologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

Diverse chancelloriids from two sections of the Kaili Biota (Cambrian Wuliuan Stage) in Guizhou Province, China, are systematically described. A total of 25 complete individuals were collected from calcareous silty mudstones of the Cambrian Kaili Formation and are assigned to 3 genera and 6 species, including Archiasterella anchoriformis sp. nov., Chancelloria zhaoi sp. nov., C. eros, Allonnia erjiensis, Al. phrixothrix, and Al. sp. The new species Ar. anchoriformis with sclerites characterised by a large angle between two marginal-lateral rays and an obvious apical tuft represents the first unambiguous Archiasterella scleritome in South China. The C. zhaoi is dominated by a series of bilaterally symmetrical, rosette-like sclerites that composed of five or six lateral rays and a central ray. Moreover, based on a careful survey of the spatial-temporal distribution of chancelloriids (including both scleritome and isolated sclerite fossils) in South China, two flourishing ages, though may be related to preservational bias, of this metazoan group are recognised: 1) a significant diversification in the upper Stage 2 to Stage 3 and 2) a thriving in the Wuliuan Stage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.231
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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